Video CoM: Diverse Video Manipulation Methods

Updated 5 July 2026

Video CoM is a multifaceted area encompassing interactive reasoning, composed retrieval, editing, and compression, offering structured operations over video segments.
Key methodologies include temporal segmentation, contrastive learning, cross-attention fusion, and token compaction, which yield measurable improvements in retrieval and editing metrics.
The field leverages iterative evidence gathering, scene graph formulation, and compressed-domain inference to enhance temporal consistency and maintain content fidelity.

In current arXiv usage, “Video CoM” is not a single canonical task but an overloaded label spanning several nearby research programs. The term appears most directly in interactive video reasoning via a Chain of Manipulations, but adjacent literature also uses closely related names for composed video retrieval, multimodal video composition/editing, video token compaction and cross-modality coding, and common/unique or motion-content factorization (Rasheed et al., 28 Nov 2025, Gupta et al., 5 Jun 2025, Gong et al., 2024, Liu et al., 20 May 2025, Sun et al., 2024). A plausible unifying theme is the replacement of one-shot monolithic video processing by structured operations over temporal segments, frames, spatial regions, multimodal side information, or decomposed latent factors.

1. Terminological scope

The most explicit usage is “Video-CoM: Interactive Video Reasoning via Chain of Manipulations”, where CoM denotes a reasoning policy that repeatedly manipulates the video during inference (Rasheed et al., 28 Nov 2025). However, neighboring papers use “CoM,” “COM,” “Com,” or similar abbreviations for substantially different objects: composed retrieval, composition/editing, compression, common-signal decomposition, or even unrelated benchmark names.

Usage	Core idea	Representative papers
Interactive reasoning	Manipulate video while reasoning	(Rasheed et al., 28 Nov 2025, Qi et al., 2023)
Composed retrieval	Retrieve a target video from source video + modification text	(Gupta et al., 5 Jun 2025, Thawakar et al., 19 Aug 2025)
Video composition/editing	Plan or generate edits/effects/compositions	(Gong et al., 2024, Tao et al., 2024, Wang et al., 2024)
Compression/coding	Compact video tokens or code video via multimodal priors	(Liu et al., 20 May 2025, Zhang et al., 2024, Xu et al., 2021)
Common/unique or motion coupling	Factorize persistent versus varying video signals	(Sun et al., 2024, Zhao et al., 15 Jan 2026, Meric et al., 21 May 2026)

This dispersion is not merely terminological. Some papers treat CoM as a reasoning policy, some as a retrieval composition operator, some as video editing or compositing, and some as compression or factorization. This suggests that “Video CoM” is best read contextually rather than as a stable field-wide acronym.

2. Composed video retrieval

In retrieval-oriented usage, Video CoM is most closely aligned with Composed Video Retrieval (CoVR). TF-CoVR defines retrieval over triplets $(V_q, T_m, V_t)$ , where $V_q$ is a query video, $T_m$ is a modification text, and $V_t$ is the target video; the pair $\langle V_q, T_m\rangle$ is the composed query (Gupta et al., 5 Jun 2025). The paper emphasizes temporally fine-grained modifications in gymnastics and diving, rather than appearance-only edits. Its benchmark contains 180K training triplets, 473 test queries, 306 fine-grained sports actions, and 3.94 valid targets on average per test query, which is why it evaluates with mAP@5/10/25/50 rather than only Recall@K (Gupta et al., 5 Jun 2025).

The proposed TF-CoVR-Base uses a two-stage design: pre-train AIM on 306 action classes for temporally discriminative video embeddings, then align composed queries and target videos with contrastive learning. Quantitatively, it improves zero-shot mAP@50 from 5.92 to 7.51, and after fine-tuning raises the state of the art from 19.83 to 25.82 (Gupta et al., 5 Jun 2025). The paper’s main substantive claim is that fine-grained CoVR depends on temporal action semantics such as twist count, somersault count, direction, and apparatus, and that generic caption-style pipelines fail to preserve these distinctions.

A larger-scale continuation appears in “Beyond Simple Edits: Composed Video Retrieval with Dense Modifications”, which introduces Dense-WebVid-CoVR with 1.6 million samples and much denser relative descriptions (Thawakar et al., 19 Aug 2025). The benchmark reports average description length 81.32 words and average modification length 31.16 words, versus 6.68 and 4.6 in WebVid-CoVR, respectively. The proposed model uses Cross-Attention fusion with a grounded text encoder and reaches 71.3\% Recall@1 in the visual+text setting, outperforming the prior state of the art by 3.4\% (Thawakar et al., 19 Aug 2025). A common misconception in this area is to treat CoVR as ordinary text-video retrieval; the cited formulations instead require a relative transformation from a source video to a target video.

3. Multimodal composition and editing

A second major usage concerns video composition in the editing sense. VCoME formulates verbal video composition with multimodal editing effects as autoregressive generation over segment-indexed effect tuples $y_i = \big(i, (l_1,e_1), (l_2,e_2), \dots \big)$ , where $l$ is a trigger position in the verbal content and $e$ is an effect name (Gong et al., 2024). Inputs are sentence-level multimodal segments $x_i=\{s_i,v_i,a_i\}$ , and the output effect vocabulary spans text animation, text effect, text template, image stickers, and sound effects. The dataset contains about 53,000 instances, split into 51,000 training and 2,100 validation samples. With prompt control, the best reported main result is 138.03 overall, and the user study claims outputs of professional quality while being 85× more efficient than professional editors (Gong et al., 2024). This is planning and recommendation over an existing verbal video, not raw video generation from scratch.

MotionCom addresses a different composition regime: training-free motion-aware image composition using an LVLM for placement planning and a video diffusion prior for motion-infused synthesis (Tao et al., 2024). It plans an insertion region $R^*$ and split ratio $V_q$ 0 with GPT-4V, constructs an intermediate composition, then applies MotionPaint, a masked latent blending procedure over a pretrained image-to-video model. The output is ultimately a single selected frame from a generated video rather than a full edited video. This matters because the method imports video priors into composition without becoming a full video-compositing pipeline.

MVOC is the closest to literal video object composition. It is a training-free multiple video object composition method that performs DDIM inversion on each source object video, composes a first frame with an image editing method, and then generates the composited video with a Video Object Dependence Module that injects object features and attention maps into an image-to-video model (Wang et al., 2024). The method is designed to preserve identity consistency, motion consistency, and temporal consistency while allowing interaction effects among non-independent objects. On the reported benchmark, it achieves the best average short- and long-range warping errors, 0.0010 and 0.0014, respectively (Wang et al., 2024). A recurrent misconception in this literature is to equate video composition with framewise blending; these papers instead treat it as structured planning or conditioned generation over temporally coherent content.

4. Interactive reasoning and multimodal prompting

The most literal “Video-CoM” formulation is interactive video reasoning via Chain of Manipulations. At reasoning step $V_q$ 1, Video-CoM generates $V_q$ 2, where $V_q$ 3 includes both reasoning tokens and a manipulation $V_q$ 4, and the next visual state is $V_q$ 5 (Rasheed et al., 28 Nov 2025). The three atomic manipulations are find-segment, find-frame, and spatial-zoom. The system is trained with Video-CoM-Instruct, an 18K instruction-tuning dataset with 15K SFT and 3K GRPO samples, and then optimized with reasoning-aware GRPO, whose total reward is $V_q$ 6 (Rasheed et al., 28 Nov 2025). The reported result is 68.7 on Video-CoM-Bench and an average gain of 3.6 percent over recent state-of-the-art models, while using only 25K SFT and 3K GRPO video samples (Rasheed et al., 28 Nov 2025). The paper’s defining claim is that video MLLMs should “think with videos,” not merely “think about videos.”

A related but tuning-free line is VidCoM, which converts selected video events into scene graphs and descriptive captions, then lets an LLM reason over those event representations (Qi et al., 2023). Its InsOVER algorithm first initializes events by moving-average clustering over CLIP frame embeddings, then refines them by Hungarian matching between OpenIE-decomposed instruction assertions and visual sub-events. On STAR VideoQA, the ChatGPT-based variant reaches mean accuracy 50, outperforming few-shot Flamingo-80B; on ActivityNet-Captions DVC, it reaches 3.7 SODA under predicted proposals and 6.6 SODA under true proposals (Qi et al., 2023). Here again, the salient move is explicit event selection and iterative evidence focus, rather than treating the entire video as a fixed context.

In robotics, Chain-of-Modality (CoM) uses video plus EMG or audio and hand pose to extract manipulation plans from a single multimodal human demonstration (Wang et al., 17 Apr 2025). The prompting order is force/audio, then hand pose, then image/video, and the outputs are skill sequences plus control parameters such as force intensity, direction, angle, and hand assignment. The paper reports 60% accuracy for extracting exact task plans and control parameters, compared with 0% for vision-only methods, and about 73% average success in real-robot execution (Wang et al., 17 Apr 2025). Although this is not the same task as Video-CoM (Rasheed et al., 28 Nov 2025), both papers replace static video interpretation with staged evidence gathering and intermediate control decisions.

5. Compression, coding, and compressed-domain inference

Another strong usage of “Video CoM” concerns video compression or compaction. VidCom $V_q$ 7 is explicitly described as a training-free, plug-and-play visual token compaction method for VideoLLMs that operates entirely at inference time (Liu et al., 20 May 2025). It does not compress pixels like a codec; it compresses the visual token sequence passed into the LLM. Its pre-LLM operator first measures frame uniqueness, allocates per-frame retention ratios, and then keeps Top- $V_q$ 8 tokens within each frame according to a combined frame-local and video-global uniqueness score. On LLaVA-OV-7B at 25% visual token retention, VidCom $V_q$ 9 achieves 99.6% of the original average performance while reducing LLM generation latency by 70.8% (Liu et al., 20 May 2025). The paper’s three design principles are Model Adaptability, Frame Uniqueness, and Operator Compatibility, and it is explicit that the method is token selection/pruning, not token merging.

At the codec level, Cross-Modality Video Coding (CMVC) decomposes a video into keyframes $T_m$ 0 and inter-keyframe motion $T_m$ 1, maps them into alternative modalities via $T_m$ 2 and $T_m$ 3, and reconstructs with $T_m$ 4 (Zhang et al., 2024). The paper instantiates TT2V (Text-Text-to-Video) for semantic reconstruction and IT2V (Image-Text-to-Video) for perceptual consistency. TT2V is framed as effective for semantic reconstruction at ultra-low bitrate, while IT2V is reported to achieve competitive perceptual consistency, including a BD-rate of -65.71 on Class B under DISTS in one configuration (Zhang et al., 2024). The motion side is chiefly represented as textual descriptions, not classical motion vectors.

A compressed-domain inference counterpart is CoVOS, which accelerates semi-supervised VOS by using frame types, motion vectors, and residuals from the compressed bitstream (Xu et al., 2021). It runs the heavy base VOS model only on I- and P-frames, then propagates masks to non-keyframes with bi-directional, multi-hop motion-vector warping and selectively corrects failures in high-residual regions. The paper reports speed-ups of up to 3.5X with minor drops in accuracy across DAVIS17 and YouTube-VOS, and on DAVIS17 STCN + CoVOS reaches 82.4 $T_m$ 5 at 33.7 FPS (Xu et al., 2021). A common misconception is to equate compressed-domain video reasoning with simply decoding RGB first; these papers instead treat the bitstream structure itself as a computational prior.

6. Factorized generative uses and non-equivalent adjacent meanings

Some “CoM/COM” papers are about factorizing persistent and varying video structure rather than retrieval, reasoning, or compression. COMUNI decomposes video into common and unique signals using CU-VAE and generates with CU-LDM (Sun et al., 2024). The supplied appendix emphasizes that keeping conditional latents fixed during iterative generation yields the best long-video behavior; among the tested strategies, fixing all conditional latent features gives the best reported FVD, 6032.1, and the best content consistency (Sun et al., 2024). Here, the “common” component corresponds to temporally shared content, while the “unique” component carries frame-specific variation.

CoMoVi pushes factorization into explicit joint generation of 3D human motion and RGB video (Zhao et al., 15 Jan 2026). It couples two video diffusion branches with mutual feature interaction and 3D-2D cross-attention, using a rendered 2D human motion representation to inherit pretrained VDM priors. On the CoMoVi Dataset test set it reports FID 0.349, R@1 0.565, R@3 0.640, and MMDist 3.035 for motion generation, while also improving several VBench video metrics over Wan2.2-I2V-5B (Zhao et al., 15 Jan 2026). In a related controllable-generation direction, CoMoGen takes a single input image and a binary mask sequence, identifies Motion Layers in MMDiT, and fine-tunes only those layers with LoRA; the added parameter cost is reported as 1.84% at inference and 3.17% during training (Meric et al., 21 May 2026).

Other acronym-nearby usages are even less directly related. COM Kitchens is a benchmark name rather than a CoM method; it introduces Online Recipe Retrieval (OnRR) and Dense Video Captioning on unedited Overhead-View videos (DVC-OV) on 145 annotated videos / 40 hours collected from 70 kitchens and 70 unique actors (Maeda et al., 2024). Outside video-language modeling, CoM may simply mean center of mass, as in 3D biped balance control with ZMP and CoM height variation (Caron et al., 2017), and Comyco denotes a quality-aware ABR controller that optimizes a VMAF-based QoE via imitation learning rather than any video reasoning or composition task (Huang et al., 2019). This suggests that acronym overlap should not be treated as conceptual equivalence: in current literature, “Video CoM” names a family of distinct, only partly overlapping ideas rather than a single research problem.